Integrating Optical Imaging Tools for Rapid and Non-invasive Characterization of Seed Quality: Tomato (Solanum lycopersicum L.) and Carrot (Daucus carota L.) as Study Cases
| Autor: | Valter Arthur, Clíssia Barboza da Silva, Marcia Eugenia Amaral Carvalho, Welinton Yoshio Hirai, Vivian Aparecida Brancaglioni, Patricia A. Galletti |
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| Rok vydání: | 2020 |
| Předmět: |
chlorophyll fluorescence
biology Multispectral image seedlots Plant Science lcsh:Plant culture chemometrics biology.organism_classification Random forest Chemometrics Horticulture machine learning Principal component analysis multispectral imaging lcsh:SB1-1110 Cultivar Solanum Chlorophyll fluorescence random forest Daucus carota Mathematics |
| Zdroj: | Frontiers in Plant Science, Vol 11 (2020) |
| ISSN: | 1664-462X |
| DOI: | 10.3389/fpls.2020.577851 |
| Popis: | Light-based methods are being further developed to meet the growing demands for food in the agricultural industry. Optical imaging is a rapid, non-destructive, and accurate technology that can produce consistent measurements of product quality compared to conventional techniques. In this research, a novel approach for seed quality prediction is presented. In the proposed approach two advanced optical imaging techniques based on chlorophyll fluorescence and chemometric-based multispectral imaging were employed. The chemometrics encompassed principal component analysis (PCA) and quadratic discrimination analysis (QDA). Among plants that are relevant as both crops and scientific models, tomato, and carrot were selected for the experiment. We compared the optical imaging techniques to the traditional analytical methods used for quality characterization of commercial seedlots. Results showed that chlorophyll fluorescence-based technology is feasible to discriminate cultivars and to identify seedlots with lower physiological potential. The exploratory analysis of multispectral imaging data using a non-supervised approach (two-component PCA) allowed the characterization of differences between carrot cultivars, but not for tomato cultivars. A Random Forest (RF) classifier based on Gini importance was applied to multispectral data and it revealed the most meaningful bandwidths from 19 wavelengths for seed quality characterization. In order to validate the RF model, we selected the five most important wavelengths to be applied in a QDA-based model, and the model reached high accuracy to classify lots with high-and low-vigor seeds, with a correct classification from 86 to 95% in tomato and from 88 to 97% in carrot for validation set. Further analysis showed that low quality seeds resulted in seedlings with altered photosynthetic capacity and chlorophyll content. In conclusion, both chlorophyll fluorescence and chemometrics-based multispectral imaging can be applied as reliable proxies of the physiological potential in tomato and carrot seeds. From the practical point of view, such techniques/methodologies can be potentially used for screening low quality seeds in food and agricultural industries. |
| Databáze: | OpenAIRE |
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